SAS^® Analytics for IoT

SAS Analytics for IoT Features List

Streamlined, extensible ETL

Automatically transforms and loads key data fields into the sensor-based data model.
Lets you rapidly load IoT data, whether you have three fields (sensor ID, value and date time) or hundreds.
Includes sensor attributes, device attributes, hierarchies, measures and events.
Enables integration of additional field and production quality data with your sensor data, using comprehensive ETL capabilities.
Connects directly to SAS Event Stream Processing to integrate real-time information with historical records.

Provides a standardized, extensible sensor-based data model.
Integrates real-time and historical data, hierarchies and other relationships right out of the box.
Organizes large volumes of diverse IoT data for efficient analysis.
Provides a single version of the data for diverse users across the organization.

Lets nontechnical users quickly select data for analysis without knowledge of the underlying technology and data structure.
Enables users to access available variables and attributes in their own business terminology.
Uses smart filters, predefined date windows and other shortcuts to increase efficiency and reduce errors.
Supports individual user needs by letting them select data for any combination of devices, sensors, measures and events.
Allows you to save, copy, reuse and share data selections across the organization.

Summarizes huge volumes of high-frequency data to understand where data are being collected and what is available for analysis.  
Reduces millions of sensor and event records to a manageable size while maintaining relationships and patterns within the data.
Visualizes IoT data to see time-series relationships among devices, events and sensor readings.

Enables users to easily prepare and transform data for analysis in SAS or third-party tools.
Transposes data from an efficient storage format to an analytics-ready format.
Interpolates missing values in the data.
Applies a fixed periodicity to reduce data size or commonize across sensors.
Lets users open the data in SAS Visual Analytics, SAS Visual Data Mining and Machine Learning, and SAS Studio, as well as third-party and open source applications.

Combines data exploration, feature engineering and modern statistical, data mining and machine learning techniques in a single, scalable in-memory processing environment.
Lets users analyze data without writing code, using a drag-and-drop interactive interface.
Uses best practice templates (basic, intermediate or advanced) to get you started quickly with machine learning tasks.
Applies diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines.
Compares results of multiple machine learning algorithms with standardized tests to automatically identify champion models.

Analyzes and filters streaming data (data in motion) in real time.
Lets you create, deploy and manage advanced analytics models running on streaming data.
Scores data in real time and applies learning models that combine scoring and training.
Reduces downstream processing by cleansing, standardizing and filtering livestream data before it’s stored.

Allows external systems to access data in a way that optimizes IoT investments across the enterprise.
Lets you integrate SAS or third-party solutions into your IoT ecosystem.
Automatically populates external dashboards and reports with the latest data or lists of data selections.